Historically, reconnaissance information has provided important information used in planning military operations. For example, prior to the advent of photography, scouts would be sent out to collect information regarding natural resources, such as lakes and rivers, enemy troop information, and the like. With the advent of photography, these scouts would provide reconnaissance information by capturing a scene of enemy installations, battlefields, and the like, using photographs.
As technology advances, new methods have been devised for collecting reconnaissance information. For example, reconnaissance planes, manned or remotely controlled, or satellites are commonly used to capture a scene in the form of image data for reconnaissance purposes. As the scene may need to be captured at any time of the day or night, in lieu of or in addition to conventional photographic techniques, it may be preferable to utilize infrared detectors and the like.
Infrared (IR) images are ideally suited for producing images of scenes captured under low light intensity. However, IR images are not always optimal. IR images are often plagued by poor contrast, which can be challenging for users trying to understand situational objects within the captured scene and make corresponding tactical decisions, for example. Typically, a simple summation of a sequence of short wave infrared images (SWIR) tends to provide some improvement in the signal-to-noise ratio of the images. However, this summation generally blurs the images and consequently has not proven to be an optimal solution.
In addition, many low-cost visible and thermal detectors or sensors spatially or electronically undersample an image. Undersampling can result in aliased images of the scene which may dilute the high-frequency components in the images. Consequently, subtle detailed information (high-frequency components) can be lost in the images.
Therefore, there is a need to remedy the problems noted above and others previously experienced for enhancing low-intensity IR images for use, in particular SWIR images, in target discrimination and identification, as well as reducing noise and improving image interpretability.